fdapace: CreateOutliersPlot – R documentation

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CreateOutliersPlot

Functional Principal Component or Functional Singular Value Decomposition Scores Plot using 'bagplot' or 'KDE' methodology

Description

This function will create, using the first components scores, a set of convex hulls of the scores based on 'bagplot' or 'KDE' methodology.

Usage

CreateOutliersPlot(fObj, optns = NULL, ...)

Arguments

`fObj`	A class object returned by FPCA() or FSVD().
`optns`	A list of options control parameters specified by `list(name=value)`. See ‘Details’.
`...`	Additional arguments for the 'plot' function.

Details

Available control options are

ifactor: inflation ifactor for the bag-plot defining the loop of bag-plot or multiplying ifactor the KDE pilot bandwidth matrix. (see ?aplpack::compute.bagplot; ?ks::Hpi respectively; default: 2.58; 2 respectively).
variant: string defining the outlier method used ('KDE', 'NN' or 'bagplot') (default: 'KDE')
unimodal: logical specifying if the KDE estimate should be unimodal (default: FALSE, relevant only for variant='KDE')
maxVar: logical specifying if during slicing we should used the directions of maximum variance (default: FALSE for FPCA, TRUE for FSVD)
nSlices: integer between 3 and 16, denoting the number of slices to be used (default: 4, relevant only for groupingType='slice')
showSlices: logical specifying if during slicing we should show the outline of the slice (default: FALSE)
colSpectrum: character vector to be use as input in the 'colorRampPalette' function defining the outliers colours (default: c("red", "yellow", 'blue'), relevant only for groupingType='slice')
groupingType: string specifying if a slice grouping ('slice') or a standard percentile/bagplot grouping ('standard') should be returned (default: 'standard')
fIndices: a two-component vector with the index of the mode of variation to consider (default: c(1,2) for FPCA and c(1,1) for FSVD)

Value

An (temporarily) invisible copy of a list containing the labels associated with each of sample curves.

References

P. J. Rousseeuw, I. Ruts, J. W. Tukey (1999): The bagplot: a bivariate boxplot, The American Statistician, vol. 53, no. 4, 382-387 R. J. Hyndman and H. L. Shang. (2010) Rainbow plots, bagplots, and boxplots for functional data, Journal of Computational and Graphical Statistics, 19(1), 29-45

Examples

set.seed(1)
n <- 420
pts <- seq(0, 1, by=0.05)
sampWiener <- Wiener(n, pts)
sampWiener <- Sparsify(sampWiener, pts, 10)
res <- FPCA(sampWiener$Ly, sampWiener$Lt, 
            list(dataType='Sparse', error=FALSE, kernel='epan', verbose=TRUE))
CreateOutliersPlot(res)

fdapace

Functional Data Analysis and Empirical Dynamics

v0.5.6

BSD_3_clause + file LICENSE

Authors

Cody Carroll [aut, cre] (<https://orcid.org/0000-0003-3525-8653>), Alvaro Gajardo [aut], Yaqing Chen [aut], Xiongtao Dai [aut], Jianing Fan [aut], Pantelis Z. Hadjipantelis [aut], Kyunghee Han [aut], Hao Ji [aut], Shu-Chin Lin [ctb], Paromita Dubey [ctb], Hans-Georg Mueller [cph, ths, aut], Jane-Ling Wang [cph, ths, aut]

Initial release

2021-01-10,